我目前正在使用TensorFlow在Python工作，并希望通过梯度下降模型而不是随机梯度下降模型来训练我的模型。原因是我想在所有数据点而不是子集上训练我的模型。考虑到我在以下位置找到的张量的不同优化器：我不确定是否可能。有人可以向我解释一下吗？
当前，代码看起来像这样：
model.compile(optimizer = 'adam', loss = 'mse')
我可以填写梯度下降优化器吗？
谢谢你！
I'm currently working in python with tensorflow and would like to train my model with a gradient descent model and not a stochastic gradient descent model. The reason is that I want to train my model on all data points instead of a subset. Considering the different optimizers for tensorflow I found at: https://www.tensorflow.org/api_docs/python/tf/keras/optimizers I'm not sure if this is possible. Could someone explain me if there is way to do this?
Currently the code looks something like this:
model.compile(optimizer = 'adam', loss = 'mse')
Is there a gradient descent optimizer I could just fill in?
Thank you!
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